title: Detection of sleep-disordered breating with Pressure Bed Sensor.
abstract: A Pressure Bed Sensor (PBS) can offer an unobtrusive method for sleep monitoring. This study focuses on the detection of the sleep related breathing disorders using a PBS in comparison to the methods used in a sleep laboratory. A newly developed PCA modeling approach for the eight sensor signals of the PBS is evaluated using the Reduced Respiratory Amplitude Index (RRAI) as a central measure. The method computes the respiration amplitude with the Hilbert transform, and then detects the events based on a 20% amplitude reduction from the baseline signal. A similar calculation was used for the sleep laboratory RIP measurements, and both PBS and RIP were compared against the reference based on the nasal flow signal. In the reference RRAI method, the respiratory-disordered events were obtained using RemLogic respiration analyzer to detect over 50% amplitude reduction in the nasal respiratory flow, but removing the RemLogic standard hypopnea event associations on the oxygen desaturation events and the sleep arousals. The movement artifacts were automatically detected based on the movement activity signal of the PBS. Twenty-five (25) out of 28 patients were finally analysed. On average 87% of a night measurement has been covered by the system. The correlation coefficient was 0.92 between the PBS and the reference RRAI, and the performance of the PBS was similar with the RIP belts. Classifying the severity of the sleep related breathing by dividing RRAI in groups according to the severity criteria, the sensitivity was 92% and the specificity was 70% for the PBS. The results suggest that PBS recording can provide an easy and un-obstructive alternative method for the detection of the sleep disordered breathing and thus has a great promise for the home monitoring.
authors.forename: Guillermina
authors.initials: G
authors.lastname: Guerrero
authors.forename: Juha M
authors.initials: JM
authors.lastname: Kortelainen
authors.forename: Elvia
authors.initials: E
authors.lastname: Palacios
authors.forename: Anna M
authors.initials: AM
authors.lastname: Bianchi
authors.forename: Giulia
authors.initials: G
authors.lastname: Tachino
authors.forename: Mirja
authors.initials: M
authors.lastname: Tenhunen
authors.forename: Martin O
authors.initials: MO
authors.lastname: Méndez
authors.forename: Mark
authors.initials: M
authors.lastname: van Gils
mesh_terms: D000465:Algorithms; D001450:Ballistocardiography / Q000295:instrumentation; D001513:Beds; D005260:Female; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000379:methods*; D011312:Pressure; D025341:Principal Component Analysis; D012119:Respiration; D012891:Sleep Apnea Syndromes / Q000175:diagnosis* / Q000503:physiopathology; D012895:Sleep, REM